storj/satellite/audit/reservoir_test.go
paul cannon 231c783698 satellite/audit: fix reservoir sampling bias
While researching logs from a large set of audits, I noticed that nearly
all of them had streamIDs starting with 0 or 1. This seemed very odd,
because streamIDs are supposed to be pretty much entirely random, and
every hex digit from 0-f should have been represented with roughly equal
frequency.

It turned out that our A-Chao implementation of reservoir sampling is
flawed. As far as we can tell, so is the Wikipedia implementation. No
one has yet reviewed the original 1982 paper by Dr. Chao in enough
detail to know where the error originated, but we do know that we have
been auditing segments near the beginning of the segment loop (low
streamIDs) far more often than segments near the end of the segment loop
(high streamIDs).

This change uses an algorithm Wikipedia calls "A-Res" instead, and adds
a test to check for that sort of bias creeping back in somehow. A-Res
will be slightly slower than A-Chao, because of a few extra steps that
need to be done, but it does appear to be selecting items uniformly.

Change-Id: I45eba4c522bafc729cebe2aab6f3fe65cd6336be
2022-12-09 18:16:58 -06:00

152 lines
4.3 KiB
Go

// Copyright (C) 2021 Storj Labs, Inc.
// See LICENSE for copying information.
package audit
import (
"encoding/binary"
"math/rand"
"sort"
"testing"
"time"
"github.com/stretchr/testify/assert"
"github.com/stretchr/testify/require"
"storj.io/common/testrand"
"storj.io/common/uuid"
"storj.io/storj/satellite/metabase"
"storj.io/storj/satellite/metabase/segmentloop"
)
func TestReservoir(t *testing.T) {
rng := rand.New(rand.NewSource(0))
r := NewReservoir(3)
seg := func(n byte) *segmentloop.Segment { return &segmentloop.Segment{StreamID: uuid.UUID{0: n}} }
// if we sample 3 segments, we should record all 3
r.Sample(rng, seg(1))
r.Sample(rng, seg(2))
r.Sample(rng, seg(3))
require.Equal(t, r.Segments[:], []segmentloop.Segment{*seg(1), *seg(2), *seg(3)})
}
func TestReservoirWeights(t *testing.T) {
var weight10StreamID = testrand.UUID()
var weight5StreamID = testrand.UUID()
var weight2StreamID = testrand.UUID()
var weight1StreamID = testrand.UUID()
streamIDCountsMap := map[uuid.UUID]int{
weight10StreamID: 0,
weight5StreamID: 0,
weight2StreamID: 0,
weight1StreamID: 0,
}
segments := []*segmentloop.Segment{
{
StreamID: weight10StreamID,
Position: metabase.SegmentPosition{},
ExpiresAt: nil,
EncryptedSize: 10,
},
{
StreamID: weight5StreamID,
Position: metabase.SegmentPosition{},
ExpiresAt: nil,
EncryptedSize: 5,
},
{
StreamID: weight2StreamID,
Position: metabase.SegmentPosition{},
ExpiresAt: nil,
EncryptedSize: 2,
},
{
StreamID: weight1StreamID,
Position: metabase.SegmentPosition{},
ExpiresAt: nil,
EncryptedSize: 1,
},
}
// run a large number of times in loop for bias to show up
rng := rand.New(rand.NewSource(time.Now().UnixNano()))
for i := 1; i < 100000; i++ {
r := NewReservoir(3)
for _, segment := range segments {
r.Sample(rng, segment)
}
for _, segment := range r.Segments {
streamIDCountsMap[segment.StreamID]++
}
// shuffle the segments order after each result
rng.Shuffle(len(segments),
func(i, j int) {
segments[i], segments[j] = segments[j], segments[i]
})
}
require.Greater(t, streamIDCountsMap[weight10StreamID], streamIDCountsMap[weight5StreamID])
require.Greater(t, streamIDCountsMap[weight5StreamID], streamIDCountsMap[weight2StreamID])
require.Greater(t, streamIDCountsMap[weight2StreamID], streamIDCountsMap[weight1StreamID])
}
// Sample many segments, with equal weight, uniformly distributed, and in order,
// through the reservoir. Expect that elements show up in the result set with
// equal chance, whether they were inserted near the beginning of the list or
// near the end.
func TestReservoirBias(t *testing.T) {
const (
reservoirSize = 3
useBits = 14
numSegments = 1 << useBits
weight = 100000 // any number; same for all segments
numRounds = 1000
)
rng := rand.New(rand.NewSource(time.Now().UnixNano()))
numsSelected := make([]uint64, numRounds*reservoirSize)
for r := 0; r < numRounds; r++ {
res := NewReservoir(reservoirSize)
for n := 0; n < numSegments; n++ {
seg := segmentloop.Segment{
EncryptedSize: weight,
}
binary.BigEndian.PutUint64(seg.StreamID[0:8], uint64(n)<<(64-useBits))
res.Sample(rng, &seg)
}
for i, seg := range res.Segments {
num := binary.BigEndian.Uint64(seg.StreamID[0:8]) >> (64 - useBits)
numsSelected[r*reservoirSize+i] = num
}
}
sort.Sort(uint64Slice(numsSelected))
// this delta is probably way too generous. but, the A-Chao
// implementation failed the test with this value, so maybe it's fine.
delta := float64(numSegments / 8)
quartile0 := numsSelected[len(numsSelected)*0/4]
assert.InDelta(t, numSegments*0/4, quartile0, delta)
quartile1 := numsSelected[len(numsSelected)*1/4]
assert.InDelta(t, numSegments*1/4, quartile1, delta)
quartile2 := numsSelected[len(numsSelected)*2/4]
assert.InDelta(t, numSegments*2/4, quartile2, delta)
quartile3 := numsSelected[len(numsSelected)*3/4]
assert.InDelta(t, numSegments*3/4, quartile3, delta)
quartile4 := numsSelected[len(numsSelected)-1]
assert.InDelta(t, numSegments*4/4, quartile4, delta)
}
type uint64Slice []uint64
func (us uint64Slice) Len() int { return len(us) }
func (us uint64Slice) Swap(i, j int) { us[i], us[j] = us[j], us[i] }
func (us uint64Slice) Less(i, j int) bool { return us[i] < us[j] }